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First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

Journal Article · · International Journal of Quantum Chemistry
DOI:https://doi.org/10.1002/qua.24375· OSTI ID:1395172
 [1]
  1. Univ. of Basel, Basel (Switzerland); Argonne National Lab. (ANL), Argonne, IL (United States)
+ Show Author Affiliations
A well-defined notion of chemical compound space (CCS) is essential for gaining rigorous control of properties through variation of elemental composition and atomic configurations. Here, we give an introduction to an atomistic first principles perspective on CCS. First, CCS is discussed in terms of variational nuclear charges in the context of conceptual density functional and molecular grand-canonical ensemble theory. Thereafter, we revisit the notion of compound pairs, related to each other via “alchemical” interpolations involving fractional nuclear charges in the electronic Hamiltonian. We address Taylor expansions in CCS, property nonlinearity, improved predictions using reference compound pairs, and the ounce-of-gold prize challenge to linearize CCS. Finally, we turn to machine learning of analytical structure property relationships in CCS. Here, these relationships correspond to inferred, rather than derived through variational principle, solutions of the electronic Schrödinger equation.
Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Organization:
Argonne National Laboratory, Argonne Leadership Computing Facility; USDOE
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1395172
Journal Information:
International Journal of Quantum Chemistry, Journal Name: International Journal of Quantum Chemistry Journal Issue: 12 Vol. 113; ISSN 0020-7608
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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  • von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16 https://doi.org/10.1002/qua.24912
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Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: Separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase text January 2020
Machine learning of molecular electronic properties in chemical compound space text January 2013
Accelerated materials property predictions and design using motif-based fingerprints text January 2015
Electronic Spectra from TDDFT and Machine Learning in Chemical Space text January 2015
Quantum-Chemical Insights from Deep Tensor Neural Networks text January 2016
Electronic spectra from TDDFT and machine learning in chemical space text January 2015
Machine learning of molecular electronic properties in chemical compound space text January 2013
Crystal structure representations for machine learning models of formation energies text January 2015
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties text January 2015
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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
alchemy
chemical space
density functional theory
design
machine learning